Experimental data typically allows the researcher to determine a causal relationship and is typically projectable to a larger population. This type of data are often reproducible, but it often can be expensive to do so.
Simulation Data Simulation data are generated by imitating the operation of a real-world process or system over time using computer test models. For example, to predict weather conditions, economic models, chemical reactions, or seismic activity. This method is used to try to determine what would, or could, happen under certain conditions.
The test model used is often as, or even more, important than the data generated from the simulation. For example, combining area and population data from the Twin Cities metro area to create population density data. While this type of data can usually be replaced if lost, it may be very time-consuming and possibly expensive to do so. They also help generate and sustain the body of experimental techniques, social conventions, and other ''methods" that scientists use in doing and reporting research.
Some of these methods are permanent features of science; others evolve over time or vary from discipline to discipline. Because they reflect socially accepted standards in science, their application is a key element of responsible scientific practice. Thus, many experimental techniques—such as statistical tests of significance, double-blind trials, or proper phrasing of questions on surveys—have been designed to minimize the influence of individual bias in research.
By adhering to these techniques, researchers produce results that others can more easily reproduce, which promotes the acceptance of those results into the scientific consensus. If research in a given area does not use generally accepted methods, other scientists will be less likely to accept the results. This was one of several reasons why many scientists reacted negatively to the initial reports of cold fusion in the late s.
The claims were so physically implausible that they required extraordinary proof. But the experiments were not initially presented in such a way that other investigators could corroborate or disprove them. When the experimental techniques became widely known and were replicated, belief in cold fusion quickly faded. In some cases the methods used to arrive at scientific knowledge are not very well defined.
Consider the problem of distinguishing the "facts" at the forefront of a given area of science. In such circumstances experimental techniques are often pushed to the limit, the signal is difficult to separate from the noise, unknown sources of error abound, and even the question to be answered is not well defined.
In such an uncertain and fluid situation, picking out reliable data from a mass of confusing and sometimes contradictory observations can be extremely difficult.
In this stage of an investigation, researchers have to be extremely clear, both to themselves and to others, about the methods being used to gather and analyze data. Other scientists will be judging not only the validity of the data but also the validity and accuracy of the methods used to derive those data.
The development of new methods can be a controversial process, as scientists seek to determine whether a given method can serve as a reliable source of new information. If someone is not forthcoming about the procedures used to derive a new result, the validation of that result by others will be hampered. Methods are important in science, but like scientific knowledge itself, they are not infallible. As they evolve over time, better methods supersede less powerful or less acceptable ones.
Methods and scientific knowledge thus progress in parallel, with each area of knowledge contributing to the other. One of the most ardent debates in astronomy at that time concerned the nature of what were then known as spiral nebulae—diffuse pinwheels of light that powerful telescopes revealed to be quite common in the night sky.
Some astronomers thought that these nebulae were spiral galaxies like the Milky Way at such great distances from the earth that individual stars could not be distinguished. Others believed that they were clouds of gas within our own galaxy. One astronomer who thought that spiral nebulae were within the Milky Way, Adriaan van Maanen of the Mount Wilson Observatory, sought to resolve the issue by comparing photographs of the nebulae taken several years apart.
After making a series of painstaking measurements, van Maanen announced that he had found roughly consistent unwinding motions in the nebulae. The detection of such motions indicated that the spirals had to be within the Milky Way, since motions would be impossible to detect in distant objects. Van Maanen's reputation caused many astronomers to accept a galactic location for the nebulae.
A few years later, however, van Maanen's colleague Edwin Hubble, using the new inch telescope at Mount Wilson, conclusively demonstrated that the nebulae were in fact distant galaxies; van Maanen's observations had to be wrong. Studies of van Maanen's procedures have not revealed any intentional misrepresentation or sources of systematic error.
Rather, he was working at the limits of observational accuracy, and his expectations influenced his measurements.
They may also incorporate information related to fiscal purposes e. Data forms will need to be customised to the type of processing and the factory management system. Discussion of implications — what is the meaning of your results? Primary Data Collection Methods Primary data collection methods can be divided into two groups: quantitative and qualitative. Monitoring off-loading catch in processed or whole round form requires considerable attention to detail and much depends on the relationship between the fishery authority and vessel captains or companies. Confidentiality is the key to the widespread acceptance of VMS, as information on current fishing grounds, and therefore security of position information, is a major concern.
Processing companies should provide basic data on the type of processing, type of raw material, capacity of processing, and even the source of material. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis. Need Help Locating Statistics? The tasks of an observer are difficult and adequate training and supervision are therefore essential.
In this stage of an investigation, researchers have to be extremely clear, both to themselves and to others, about the methods being used to gather and analyze data. Fishing companies are often a good source of information regarding basic data on catches and fishing effort. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge. It can be very difficult to estimate the total fish weight, let alone weight by species, product and size grade. Summary of findings — synthesize the answers to your research questions. For the large-scale fishery where a logbook system is used, data collected at landing sites could be used to crosscheck data recorded in logbooks.
In general, writing should be reduced to a minimum e. The test model used is often as, or even more, important than the data generated from the simulation. When they get back to their own laboratory and examine the data, they get the following data points. For example, catch or landing information can be collected through questionnaire from fishers, market middle-persons, market sellers and buyers, processors etc. During the measurements at the national laboratory, Deborah and Kathleen observed that there were power fluctuations they could not control or predict. Among the specific strengths of using quantitative methods to study social science research problems: Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results; Allows for greater objectivity and accuracy of results.
Participant-observation is a technique whereby the researcher spends an extended period of time from weeks to years, depending on the objective and the context living with a target community, both observing their behaviour and participating in their practices. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. If research in a given area does not use generally accepted methods, other scientists will be less likely to accept the results.
Collecting data to estimate raising factors for converting landed processed fish weight to the whole weight equivalent may be necessary. London: Sage, ; Gay,L. Enumerators can be mobile that is sites are visited on a rotational basis or resident at a specific sampling site. With disciplines in which experimentation is less straightforward, such as geology, astronomy, or many of the social sciences, good hypotheses should be able to unify disparate observations. Methods are important in science, but like scientific knowledge itself, they are not infallible.
Choose a minimally sufficient statistical procedure; provide a rationale for its use and a reference for it. If the questionnaire is used for a complete enumeration, then special care needs to be taken to avoid overburdening the respondent. If trade data are used for validating or estimating landings, the quantities will usually need converting to whole weight. Types of Research Data Types of Research Data Data may be grouped into four main types based on methods for collection: observational, experimental, simulation, and derived. Questionnaires can be handed out or sent by mail and later collected or returned by stamped addressed envelope.
If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e. Their positions on fishing vessels and the tasks that they perform depend significantly on a good working relationship with the captain and crew, which can be lost if they are perceived as enforcement personnel. Qualitative research is closely associated with words, sounds, feeling, emotions, colours and other elements that are non-quantifiable. Inspectors need to be skilled in such sampling strategies. If research in a given area does not use generally accepted methods, other scientists will be less likely to accept the results.